Lecture 8

Career Pathways in Data Analytics and Beyond

Byeong-Hak Choe
bchoe@geneseo.edu

SUNY Geneseo

October 24, 2025

Career in Data Analytics

Geneseo Alumni Career Talk

Geneseo Alumni Career Talk

Q & A from Jaehyung and Jason

  1. How do your teams work with big-tech partners to provide AI models? How have generative AI tools changed your workflow?
  1. For someone who wants to start a business, which AI capabilities would you prioritize learning or adopting first?
  1. What led you to pursue a career in data analytics—was it a long-term plan or something you discovered along the way?
  1. After graduation, which career decision helped you the most?

Q & A from Jaehyung and Jason

  1. When should students begin exploring and applying for internships? What timeline do you recommend?
  • View Video

  • “The more internship, the better. The earlier, the better as well.” Jaehyung Andy Lee

  1. Which tools—and which AI tools—do you use most in your current role?
  1. What challenges have you faced at work, and how did you overcome them?

Q & A from Oliver

  1. How have generative AI tools changed your workflow at the Federal Reserve? What is your opinion on their impact — both on your own work and on the data analytics industry more broadly?
  1. What made you choose Economics as your major, and why did you decide to add Mathematics and Data Analytics as minors?
  1. You participated in Fed Challenge and FDIC Challenge during college. How did those experiences influence your learning, professional development, and career preparation?

Q & A from Oliver

  1. What kinds of backgrounds do your colleagues have at the Federal Reserve? Do they share similar academic paths to yours, or do they come from different disciplines?
  1. When you were job searching and interviewing, what qualities or skills did employers seem to value the most? Beyond technical ability, what stood out as important during your conversations with them?
  1. How was the transition between different coding languages and tools (e.g., R, Python, Alteryx, SQL) in your work?

Q & A from Oliver

  1. Looking back, how difficult was it to learn coding during college, and what strategies helped you gain confidence in programming?
  1. What aspects of your current role do you find the most rewarding or meaningful — personally or professionally?
  1. How did you adjust from being a college student to working full time? What changes in lifestyle or mindset did you experience during that transition?

Q & A from Oliver

  1. What were some of the biggest challenges you faced early in your job, and how did you manage to overcome them?
  1. When you encountered unfamiliar tasks or uncertainty at work, what kinds of support systems or resources helped you succeed?
  1. During college, what helped you develop your programming and analytical skills most effectively — coursework, practice, projects, or office hours?

Summary for Jaehyung and Jason’s Session

🧩 Roles & Workflows

  • Jaehyung (AI Enablement / Analytics at Invisible Technologies)
    • Works with AI model companies like OpenAI, Cohere, Google DeepMind
    • Manages data pipelines, automation, and analytics with 20,000+ distributed agents
    • Emphasized Streamlit apps, personal data projects, and building tools beyond class assignments

🧩 Roles & Workflows

  • Jason (Data Engineer at Momentive)
    • Works in IT data engineering, integrating ERP, Workday, travel, HR data into Snowflake
    • Builds Tableau dashboards and automated data pipelines for business decision-making
    • Transitioned from internship → full-time by demonstrating value through small data tools

💡 Lessons on Career Preparation

  • Personal Projects > Just Coursework
    • Build one deep project you can talk about—more powerful than many shallow demos
  • Internships: Ideally by junior year, but personal projects + outreach can substitute
  • Networking: Reaching out to alumni on LinkedIn works—alumni want to help
  • Data Career Reality: Messy data, stakeholder communication, iteration, and revisions matter more than “perfect code”

🤖 AI & Modern Workflow Changes

  • 60–80% of analytics/engineering code now written with AI assistants (Claude, Copilot)
  • “Vibe coding” → prompting AI to generate logic flows instead of hand-coding everything
  • Still requires human review → AI amplifies analysts, not replaces them
  • Data sensitivity & responsible AI use are now core skills (Copilot within secure environment)

🧭 Mindset & Professional Growth

  • Don’t just execute requests — ask “why” and co-design solutions with stakeholders
  • Avoid taking feedback personally — debugging business logic is normal
  • Stay adaptable — small teams + AI enable startup-style speed, even inside large companies

✅ Skills & Tools to Prioritize

  • Coding Fundamentals Matter — Python, SQL, R (to understand and correct AI-generated code)
  • Streamlit / App Building — both alumni mentioned it as a direct advantage
  • Data Pipeline Understanding — Snowflake, ETL logic, API data ingestion
    • 💡 API (Application Programming Interface): A structured way for one software or system to communicate with another — allowing programs to request data or trigger actions automatically, without using a manual interface (e.g., a browser).
    • Example: The Google Maps API lets applications access location, routing, and distance data programmatically.
  • Dashboarding / Visualization — Tableau or Shiny (used in internship-to-job pipeline)

🧠 Real Data Work = Beyond Clean CSVs

  • Messy, inconsistent corporate data is normal
  • Analysts must reshape, clean, validate, and question data
  • UAT (User Acceptance Testing) → users will question your output; don’t take it personally
  • Communication = Core skill

💬 Communication & Stakeholder Logic

  • Don’t just deliver what’s asked — ask “What decision will this data support?”
  • Stakeholders often don’t know what they need
  • You become the data advisor, not just the “SQL person”
  • Work smart, not just hard — clarify goals before coding dashboards

🧪 Personal Projects = Career Currency

  • Quality > Quantity — one deep project beats five generic Kaggle plots
    • Kaggle — a platform for data science competitions, datasets, and collaborative notebooks.
  • Jaehyung used a self-tracked life project to stand out in interviews
  • Use class projects as foundation, then extend them your own way

🎓 Internships & Entry Points

  • Common internship timing: Summer before senior year
  • No internship? → personal projects + LinkedIn outreach still work, but having internship experience would be more beneficial
  • Alumni emphasized: “If a Geneseo student messages me, I feel responsible to reply.”

🤖 How AI Actually Fits Into Data Jobs

  • 80% of daily code now AI-assisted
  • “Vibe coding” = prompting AI to write working SQL/Python snippets
  • Human oversight remains essential
  • Company policies differ — many only allow Copilot or internal AI

🛡 Data Ethics & Security Awareness

  • Never paste confidential data into public AI tools
  • Use secure AI (e.g., Microsoft Copilot) within company systems
  • Understanding governance & privacy = part of professional data practice

🧭 Career Mindset Recalibration

  • Big Tech ≠ only path — small, AI-enabled startups thrive
  • Solo founders can build functional apps with AI + data pipelines
  • “Best time in history to build something—don’t just consume.”

Summary for Oliver’s Session

💼 Oliver’s Career Path

  • Joined the Statistics Department at the Federal Reserve Bank of New York
  • Department Role:
    • Collects and processes regulatory data from banks
    • Ensures data accuracy and detects anomalies
    • Communicates with reporting institutions to verify discrepancies
  • Uses Python, SQL, and Excel daily
    • Transitioning from Alteryx to Python workflows
  • 💡 Note: Alteryx is a visual data analytics platform that allows users to prepare, clean, and analyze data through a drag-and-drop interface — often used in industry for building workflows without extensive coding.

🧠 Daily Responsibilities

  • Maintain and analyze regulatory datasets (hundreds of banks, monthly reports)
  • Perform data transformation, cleaning, and aggregation
  • Identify anomalies and follow up with banks for clarification
  • Develop efficient data pipelines and quality assurance checks

🤖 Generative AI in the Workplace

  • The Fed prohibits public AI tools (e.g., ChatGPT) due to data sensitivity
  • Uses a secure in-house generative AI system
  • Oliver’s perspective:
    • AI is excellent for routine transformations and debugging
    • Analysts still need domain understanding and context awareness
    • “You need to know what you’re asking it to do—and what the right answer looks like.”

🧮 Choosing Majors and Minors

  • Initially unsure of major; inspired by a pop economics book (e.g., Freakonomics)
  • Chose Economics for its analytical and real-world reasoning value
  • Added Mathematics and Data Analytics:
    • Enjoyed quantitative reasoning and problem-solving
    • The Data Analytics minor was newly introduced (2022)
    • Saw its strong career relevance

🏆 Experiential Learning: Competitions

  • Participated in:
    • Fed Challenge
    • FDIC (Federal Deposit Insurance Corporation) Challenge
  • Benefits:
    • Provided real-world data experience
    • Developed strong communication and teamwork skills
    • Created interview stories to discuss challenges and teamwork
    • Improved Excel visualization and data storytelling skills
  • “These challenges give you something distinctive to talk about in interviews.”

👥 Workplace Environment

  • Team composed of diverse professionals:
    • Former accountants, consultants, and government analysts
    • Includes another Geneseo alum (Maddie Maline Katz, Class of 2019)
  • Emphasis on collaboration and mentorship
  • Strong internal support for learning new tools and methods

🗣️ Q & A - 1. Interview Preparation

  • Expect standard behavioral questions
  • “You should walk into an interview knowing your answers to:
    • ‘Why this job?’
    • ‘Tell me about a time you faced a challenge.’”
  • Employers value preparation and clarity more than extensive experience.

🗣️ Q & A - 2. Transition to Work Life

  • “College gives you more free time than you’ll ever have again.”
  • Full-time work (40+ hours + commute) requires time management
  • Importance of being intentional about personal growth and hobbies

🗣️ Q & A - 3. Learning to Code

  • Transitioning between tools (R, Python, Alteryx):
    • “Once you know what you want to do with the data, syntax differences don’t matter.”
  • Key takeaway:
    • Conceptual understanding of data transformation > memorizing syntax
    • Consistent practice and projects build fluency

🗣️ Q & A - 4. Most Rewarding Aspects

  • Solving data problems and building solutions quickly
  • Satisfaction from turning requests into usable visualizations
  • Feels fulfillment as a public servant contributing to national financial oversight

🗣️ Q & A - 5. Challenges and Growth

  • Learning report-specific finance and accounting concepts
  • Interpreting large financial datasets (e.g., repurchase agreements)
  • Overcame initial struggles via:
    • Mentorship from senior staff
    • Report specialists for regulatory interpretation
    • A collaborative, knowledge-sharing workplace

🗣️ Q & A - 6. Developing Programming Skills

  • “There’s no shortcut — do your assignments.”
  • Skills built through:
    • Homework and repetition
    • Applying code in competitions (Fed Challenge)
    • Office hours and project feedback (notably DANL 300-level project)
  • Emphasized hands-on learning and visual thinking when coding

🗣️ Q & A - 🧭 Key Takeaway

  • 🔹 Start exploring early — even without clear goals, exposure builds direction
  • 🔹 Participate in challenges — showcase applied skills
  • 🔹 Prepare well for interviews — know your behavioral answers
  • 🔹 Practice coding — consistent repetition is the best teacher
  • 🔹 Use your free time wisely — college is your training ground
  • 🔹 Public sector roles offer purpose, but problem-solving passion is universal